Pluripotent stem cells can be derived from both pre- and post-implantation embryos. Embryonic stem cells (ES cells), derived from inner cell mass (ICM) of blastocyst are naïve pluripotent and epiblast stem cells (EpiSCs) derived from post-implantation epiblast are primed pluripotent. The phenotypes and gene expression patterns of the two pluripotent stem cells are different each other and EpiSCs thought to be in a more advanced pluripotent (primed pluripotent state) than mouse ES cells (naïve pluripotent state). Therefore, we questioned whether EpiSCs are less potential to be differentiated into specialized cell types in vitro. EpiSCs were isolated from 5.5～6.5 day post coitum mouse embryos of the post-implantation epiblast. The EpiSCs could differentiate into all tree germ layers in vivo, and expressed pluripotency markers (Oct4, Nanog). Interestingly, EpiSCs also were able to efficiently differentiate into neural stem cells (NSCs). The NSCs differentiated from EpiSCs (EpiSC-NSCs) expressed NSC markers (Nestin, Sox2, and Musasi), self-renewed over passage 20, and could differentiate into two neural subtypes, neurons, astrocytes and oligodendrocytes. Next, we compared global gene expression patterns of EpiSC-NSCs with that of NSCs differentiated from ES cells and brain tissue. Gene expression pattern of brain tissue derived NSCs were closer to ES cell-derived NSCs than EpiSC-NSCs, indicating that the pluripotent stem cell-derived somatic cells could have different characteristics depending on the origin of pluripotent stem cell types. * This work was supported by the Next Generation Bio-Green 21 Program funded by the Rural Development Administration (Grant PJ 008009).

Water quality in Nakdong river was analyzed using 699 monitoring data sets including flow rates and water quality concentrations collected at 195 tributary monitoring stations (the priority management areas: 35 stations, the non-priority management areas: 160 stations) in 2015. The highest average concentrations of all data for BOD, COD, T-N, T-P, SS, and TOC were 30 600 times higher than the lowest concentrations while the highest average loading rates were 800,000 2,700,000 times higher than the lowest loading rates. Because of the very large differences in the concentrations and loading rates, the variation of the concentrations and loading rates in a priority management monitoring station for BOD, T-P, and TOC was analyzed using the coefficient of variation, the ratio of the standard deviation value to the mean value. For BOD, T-P, and TOC, the coefficients of variation for concentration were mostly less than 100%, whereas the coefficients of variation for loading rate ranged from 31.1% to 232.2%. The very big difference in the loading rates was due to the large variation in flow rates. As a result of this, the estimation of water quality at each monitoring station using the average values of the concentrations and loading rates might be not rational in terms of their representativeness. In this study, new water quality analysis methods using all collected monitoring data were suggested and applied according to the water quality standard in medium-sized management areas.